{"id":"https://openalex.org/W4414360175","doi":"https://doi.org/10.24963/ijcai.2025/371","title":"Non-collective Calibrating Strategy for Time Series Forecasting","display_name":"Non-collective Calibrating Strategy for Time Series Forecasting","publication_year":2025,"publication_date":"2025-09-01","ids":{"openalex":"https://openalex.org/W4414360175","doi":"https://doi.org/10.24963/ijcai.2025/371"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2025/371","is_oa":false,"landing_page_url":"https://doi.org/10.24963/ijcai.2025/371","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5109649252","display_name":"Bin Wang","orcid":"https://orcid.org/0009-0007-9532-0005"},"institutions":[{"id":"https://openalex.org/I59028903","display_name":"Ocean University of China","ror":"https://ror.org/04rdtx186","country_code":"CN","type":"education","lineage":["https://openalex.org/I59028903"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Bin Wang","raw_affiliation_strings":["Ocean University of China"],"affiliations":[{"raw_affiliation_string":"Ocean University of China","institution_ids":["https://openalex.org/I59028903"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066725777","display_name":"Yuan\u2010Ping Han","orcid":"https://orcid.org/0000-0001-6282-7319"},"institutions":[{"id":"https://openalex.org/I59028903","display_name":"Ocean University of China","ror":"https://ror.org/04rdtx186","country_code":"CN","type":"education","lineage":["https://openalex.org/I59028903"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongqi Han","raw_affiliation_strings":["Ocean University of China"],"affiliations":[{"raw_affiliation_string":"Ocean University of China","institution_ids":["https://openalex.org/I59028903"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037786601","display_name":"Minbo Ma","orcid":"https://orcid.org/0000-0001-6098-111X"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Minbo Ma","raw_affiliation_strings":["Southwest Jiaotong University"],"affiliations":[{"raw_affiliation_string":"Southwest Jiaotong University","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070559820","display_name":"Tianrui Li","orcid":"https://orcid.org/0000-0001-7780-104X"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianrui Li","raw_affiliation_strings":["Southwest Jiaotong University"],"affiliations":[{"raw_affiliation_string":"Southwest Jiaotong University","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100778479","display_name":"Junbo Zhang","orcid":"https://orcid.org/0000-0001-5947-1374"},"institutions":[{"id":"https://openalex.org/I890469752","display_name":"Ministry of Industry and Information Technology","ror":"https://ror.org/0385nmy68","country_code":"CN","type":"government","lineage":["https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junbo Zhang","raw_affiliation_strings":["Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence","JD Intelligent Cities Research","JD iCity, JD Technology, China"],"affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence","institution_ids":["https://openalex.org/I890469752"]},{"raw_affiliation_string":"JD Intelligent Cities Research","institution_ids":[]},{"raw_affiliation_string":"JD iCity, JD Technology, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079006967","display_name":"Feng Hong","orcid":"https://orcid.org/0000-0002-4167-6037"},"institutions":[{"id":"https://openalex.org/I59028903","display_name":"Ocean University of China","ror":"https://ror.org/04rdtx186","country_code":"CN","type":"education","lineage":["https://openalex.org/I59028903"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Hong","raw_affiliation_strings":["Ocean University of China"],"affiliations":[{"raw_affiliation_string":"Ocean University of China","institution_ids":["https://openalex.org/I59028903"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021981732","display_name":"Yanwei Yu","orcid":"https://orcid.org/0000-0002-5924-1410"},"institutions":[{"id":"https://openalex.org/I59028903","display_name":"Ocean University of China","ror":"https://ror.org/04rdtx186","country_code":"CN","type":"education","lineage":["https://openalex.org/I59028903"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanwei Yu","raw_affiliation_strings":["Ocean University of China"],"affiliations":[{"raw_affiliation_string":"Ocean University of China","institution_ids":["https://openalex.org/I59028903"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5109649252"],"corresponding_institution_ids":["https://openalex.org/I59028903"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.33775702,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3335","last_page":"3343"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.7032999992370605,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.7032999992370605,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.6453999876976013,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/rule-of-thumb","display_name":"Rule of thumb","score":0.6126999855041504},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5320000052452087},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5160999894142151},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.4555000066757202},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.4020000100135803},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.39980000257492065}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7402999997138977},{"id":"https://openalex.org/C89246107","wikidata":"https://www.wikidata.org/wiki/Q1398821","display_name":"Rule of thumb","level":2,"score":0.6126999855041504},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5320000052452087},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5160999894142151},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4932999908924103},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.4555000066757202},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.447299987077713},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.4020000100135803},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.39980000257492065},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.38929998874664307},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.375},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3068000078201294},{"id":"https://openalex.org/C2780070844","wikidata":"https://www.wikidata.org/wiki/Q857815","display_name":"Plug and play","level":2,"score":0.29750001430511475},{"id":"https://openalex.org/C88516994","wikidata":"https://www.wikidata.org/wiki/Q1268863","display_name":"Dynamic time warping","level":2,"score":0.2892000079154968},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.28290000557899475},{"id":"https://openalex.org/C4924752","wikidata":"https://www.wikidata.org/wiki/Q184148","display_name":"Plug-in","level":2,"score":0.2667999863624573}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2025/371","is_oa":false,"landing_page_url":"https://doi.org/10.24963/ijcai.2025/371","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Deep":[0],"learning-based":[1],"approaches":[2],"have":[3],"demonstrated":[4],"significant":[5],"advancements":[6],"in":[7,77,192],"time":[8,19,87,162],"series":[9,20,163],"forecasting.":[10],"Despite":[11],"these":[12],"ongoing":[13],"developments,":[14],"the":[15,26,32,78,91,94,129,145,172,189],"complex":[16],"dynamics":[17],"of":[18,28,93,138,147,154,174],"make":[21],"it":[22,141],"challenging":[23],"to":[24,61,90,142,178],"establish":[25],"rule":[27],"thumb":[29],"for":[30,120],"designing":[31],"golden":[33],"model":[34,67],"architecture.":[35],"In":[36],"this":[37,100],"study,":[38],"we":[39,102],"argue":[40],"that":[41],"refining":[42],"existing":[43],"advanced":[44],"models":[45],"through":[46],"a":[47,65,73,166,179,185],"universal":[48],"calibrating":[49,79,106],"strategy":[50,107],"can":[51],"deliver":[52],"substantial":[53],"benefits":[54],"with":[55],"minimal":[56],"resource":[57],"costs,":[58],"as":[59,188],"opposed":[60],"elaborating":[62],"and":[63,117,165],"training":[64],"new":[66],"from":[68],"scratch.":[69],"We":[70],"first":[71],"identify":[72],"multi-target":[74],"learning":[75,96],"conflict":[76],"process,":[80],"which":[81],"arises":[82],"when":[83,183],"optimizing":[84],"variables":[85],"across":[86],"steps,":[88],"leading":[89],"underutilization":[92],"model's":[95],"capabilities.":[97],"To":[98],"address":[99],"issue,":[101],"propose":[103],"an":[104,114],"innovative":[105],"called":[108],"Socket+Plug":[109],"(SoP).":[110],"This":[111],"approach":[112],"retains":[113],"exclusive":[115],"optimizer":[116],"early-stopping":[118],"monitor":[119],"each":[121,125],"predicted":[122],"target":[123],"within":[124],"Plug":[126,190],"while":[127],"keeping":[128],"fully":[130],"trained":[131,149],"Socket":[132],"backbone":[133],"frozen.":[134],"The":[135],"model-agnostic":[136],"nature":[137],"SoP":[139],"allows":[140],"directly":[143],"calibrate":[144],"performance":[146],"any":[148],"deep":[150],"forecasting":[151],"models,":[152],"regardless":[153],"their":[155],"specific":[156],"architectures.":[157],"Extensive":[158],"experiments":[159],"on":[160],"various":[161],"benchmarks":[164],"spatio-temporal":[167],"meteorological":[168],"ERA5":[169],"dataset":[170],"demonstrate":[171],"effectiveness":[173],"SoP,":[175],"achieving":[176],"up":[177],"22%":[180],"improvement":[181],"even":[182],"employing":[184],"simple":[186],"MLP":[187],"(highlighted":[191],"Figure":[193],"1).":[194]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-10T00:00:00"}
